Overview

Brought to you by YData

Dataset statistics

Number of variables 9
Number of observations 768
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 54.1 KiB
Average record size in memory 72.2 B

Variable types

Numeric 8
Categorical 1

Alerts

Age is highly overall correlated with Pregnancies High correlation
Insulin is highly overall correlated with SkinThickness High correlation
Pregnancies is highly overall correlated with Age High correlation
SkinThickness is highly overall correlated with Insulin High correlation
Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 35 (4.6%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros
Insulin has 374 (48.7%) zeros Zeros
BMI has 11 (1.4%) zeros Zeros

Reproduction

Analysis started 2025-09-03 16:15:55.297964
Analysis finished 2025-09-03 16:16:11.973082
Duration 16.68 seconds
Software version ydata-profiling vv4.16.1
Download configuration config.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct 17
Distinct (%) 2.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.8450521
Minimum 0
Maximum 17
Zeros 111
Zeros (%) 14.5%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:12.141099 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 1
median 3
Q3 6
95-th percentile 10
Maximum 17
Range 17
Interquartile range (IQR) 5

Descriptive statistics

Standard deviation 3.3695781
Coefficient of variation (CV) 0.87634133
Kurtosis 0.15921978
Mean 3.8450521
Median Absolute Deviation (MAD) 2
Skewness 0.90167398
Sum 2953
Variance 11.354056
Monotonicity Not monotonic
2025-09-03T23:16:12.313143 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
Value Count Frequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
Value Count Frequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Value Count Frequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Glucose
Real number (ℝ)

Distinct 136
Distinct (%) 17.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 120.89453
Minimum 0
Maximum 199
Zeros 5
Zeros (%) 0.7%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:12.522374 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 79
Q1 99
median 117
Q3 140.25
95-th percentile 181
Maximum 199
Range 199
Interquartile range (IQR) 41.25

Descriptive statistics

Standard deviation 31.972618
Coefficient of variation (CV) 0.26446703
Kurtosis 0.64077982
Mean 120.89453
Median Absolute Deviation (MAD) 20
Skewness 0.1737535
Sum 92847
Variance 1022.2483
Monotonicity Not monotonic
2025-09-03T23:16:12.802033 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
125 14
 
1.8%
129 14
 
1.8%
106 14
 
1.8%
102 13
 
1.7%
105 13
 
1.7%
112 13
 
1.7%
95 13
 
1.7%
Other values (126) 626
81.5%
Value Count Frequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 2
 
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
Value Count Frequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Zeros 

Distinct 47
Distinct (%) 6.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 69.105469
Minimum 0
Maximum 122
Zeros 35
Zeros (%) 4.6%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:13.049370 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 38.7
Q1 62
median 72
Q3 80
95-th percentile 90
Maximum 122
Range 122
Interquartile range (IQR) 18

Descriptive statistics

Standard deviation 19.355807
Coefficient of variation (CV) 0.28009082
Kurtosis 5.1801566
Mean 69.105469
Median Absolute Deviation (MAD) 8
Skewness -1.843608
Sum 53073
Variance 374.64727
Monotonicity Not monotonic
2025-09-03T23:16:13.297683 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
Value Count Frequency (%)
70 57
 
7.4%
74 52
 
6.8%
78 45
 
5.9%
68 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
0 35
 
4.6%
Other values (37) 331
43.1%
Value Count Frequency (%)
0 35
4.6%
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
 
1.7%
52 11
 
1.4%
Value Count Frequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

High correlation  Zeros 

Distinct 51
Distinct (%) 6.6%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 20.536458
Minimum 0
Maximum 99
Zeros 227
Zeros (%) 29.6%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:13.532987 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 23
Q3 32
95-th percentile 44
Maximum 99
Range 99
Interquartile range (IQR) 32

Descriptive statistics

Standard deviation 15.952218
Coefficient of variation (CV) 0.77677549
Kurtosis -0.52007187
Mean 20.536458
Median Absolute Deviation (MAD) 12
Skewness 0.1093725
Sum 15772
Variance 254.47325
Monotonicity Not monotonic
2025-09-03T23:16:14.138891 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
18 20
 
2.6%
33 20
 
2.6%
28 20
 
2.6%
31 19
 
2.5%
39 18
 
2.3%
Other values (41) 341
44.4%
Value Count Frequency (%)
0 227
29.6%
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
 
1.4%
14 6
 
0.8%
15 14
 
1.8%
16 6
 
0.8%
Value Count Frequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

Insulin
Real number (ℝ)

High correlation  Zeros 

Distinct 186
Distinct (%) 24.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 79.799479
Minimum 0
Maximum 846
Zeros 374
Zeros (%) 48.7%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:14.387742 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 30.5
Q3 127.25
95-th percentile 293
Maximum 846
Range 846
Interquartile range (IQR) 127.25

Descriptive statistics

Standard deviation 115.244
Coefficient of variation (CV) 1.4441699
Kurtosis 7.2142596
Mean 79.799479
Median Absolute Deviation (MAD) 30.5
Skewness 2.2722509
Sum 61286
Variance 13281.18
Monotonicity Not monotonic
2025-09-03T23:16:14.632849 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 374
48.7%
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
110 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
Value Count Frequency (%)
0 374
48.7%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
 
0.3%
22 1
 
0.1%
23 2
 
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
Value Count Frequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

Zeros 

Distinct 248
Distinct (%) 32.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 31.992578
Minimum 0
Maximum 67.1
Zeros 11
Zeros (%) 1.4%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:14.929541 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 21.8
Q1 27.3
median 32
Q3 36.6
95-th percentile 44.395
Maximum 67.1
Range 67.1
Interquartile range (IQR) 9.3

Descriptive statistics

Standard deviation 7.8841603
Coefficient of variation (CV) 0.24643717
Kurtosis 3.2904429
Mean 31.992578
Median Absolute Deviation (MAD) 4.6
Skewness -0.42898159
Sum 24570.3
Variance 62.159984
Monotonicity Not monotonic
2025-09-03T23:16:15.190135 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
0 11
 
1.4%
32.4 10
 
1.3%
33.3 10
 
1.3%
32.9 9
 
1.2%
30.1 9
 
1.2%
30.8 9
 
1.2%
32.8 9
 
1.2%
Other values (238) 664
86.5%
Value Count Frequency (%)
0 11
1.4%
18.2 3
 
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
 
0.3%
19.6 3
 
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
Value Count Frequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct 517
Distinct (%) 67.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.4718763
Minimum 0.078
Maximum 2.42
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:15.423038 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.078
5-th percentile 0.14035
Q1 0.24375
median 0.3725
Q3 0.62625
95-th percentile 1.13285
Maximum 2.42
Range 2.342
Interquartile range (IQR) 0.3825

Descriptive statistics

Standard deviation 0.3313286
Coefficient of variation (CV) 0.70215138
Kurtosis 5.5949535
Mean 0.4718763
Median Absolute Deviation (MAD) 0.1675
Skewness 1.9199111
Sum 362.401
Variance 0.10977864
Monotonicity Not monotonic
2025-09-03T23:16:15.685406 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.268 5
 
0.7%
0.27 4
 
0.5%
0.263 4
 
0.5%
0.304 4
 
0.5%
Other values (507) 719
93.6%
Value Count Frequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
Value Count Frequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct 52
Distinct (%) 6.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 33.240885
Minimum 21
Maximum 81
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 6.1 KiB
2025-09-03T23:16:15.929122 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 21
5-th percentile 21
Q1 24
median 29
Q3 41
95-th percentile 58
Maximum 81
Range 60
Interquartile range (IQR) 17

Descriptive statistics

Standard deviation 11.760232
Coefficient of variation (CV) 0.35378816
Kurtosis 0.64315889
Mean 33.240885
Median Absolute Deviation (MAD) 7
Skewness 1.1295967
Sum 25529
Variance 138.30305
Monotonicity Not monotonic
2025-09-03T23:16:16.163428 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
Value Count Frequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
Value Count Frequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct 2
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 43.6 KiB
0
500 
1
268 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 768
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1
2nd row 0
3rd row 1
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Length

2025-09-03T23:16:16.432875 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-03T23:16:16.580162 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2025-09-03T23:16:09.783418 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:55.994685 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:58.493603 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:00.339119 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:02.087713 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:03.719451 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:05.472999 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:07.701368 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:09.996150 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:56.299474 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:58.738936 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:00.605229 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:02.278193 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:03.954595 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:05.711096 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:07.947999 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:10.272239 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:57.068880 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:58.970456 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:00.890116 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:02.466329 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:04.223220 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:06.237872 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:08.176865 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:10.495903 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:57.385341 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:59.190367 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:01.079355 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:02.693040 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:04.473320 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:06.430417 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:08.554144 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:10.685146 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:57.611510 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:59.417473 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:01.296053 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:02.907612 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:04.690736 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:06.687109 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:08.801681 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:10.897350 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:57.855008 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:59.603491 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:01.487320 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:03.106547 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:04.911146 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:07.005061 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:09.006806 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:11.093249 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:58.090209 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:59.839986 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:01.665971 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:03.275633 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:05.121440 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:07.272098 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:09.251128 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:11.284995 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:15:58.303248 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:00.082940 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:01.916185 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:03.529853 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:05.294056 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:07.453470 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-03T23:16:09.489098 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-03T23:16:16.700292 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Age BMI BloodPressure DiabetesPedigreeFunction Glucose Insulin Outcome Pregnancies SkinThickness
Age 1.000 0.131 0.351 0.043 0.285 -0.114 0.314 0.607 -0.067
BMI 0.131 1.000 0.293 0.141 0.231 0.193 0.317 0.000 0.444
BloodPressure 0.351 0.293 1.000 0.030 0.235 -0.007 0.152 0.185 0.126
DiabetesPedigreeFunction 0.043 0.141 0.030 1.000 0.091 0.221 0.173 -0.043 0.180
Glucose 0.285 0.231 0.235 0.091 1.000 0.213 0.487 0.131 0.060
Insulin -0.114 0.193 -0.007 0.221 0.213 1.000 0.159 -0.127 0.541
Outcome 0.314 0.317 0.152 0.173 0.487 0.159 1.000 0.235 0.208
Pregnancies 0.607 0.000 0.185 -0.043 0.131 -0.127 0.235 1.000 -0.085
SkinThickness -0.067 0.444 0.126 0.180 0.060 0.541 0.208 -0.085 1.000

Missing values

2025-09-03T23:16:11.564343 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-03T23:16:11.796744 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
5 5 116 74 0 0 25.6 0.201 30 0
6 3 78 50 32 88 31.0 0.248 26 1
7 10 115 0 0 0 35.3 0.134 29 0
8 2 197 70 45 543 30.5 0.158 53 1
9 8 125 96 0 0 0.0 0.232 54 1
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
758 1 106 76 0 0 37.5 0.197 26 0
759 6 190 92 0 0 35.5 0.278 66 1
760 2 88 58 26 16 28.4 0.766 22 0
761 9 170 74 31 0 44.0 0.403 43 1
762 9 89 62 0 0 22.5 0.142 33 0
763 10 101 76 48 180 32.9 0.171 63 0
764 2 122 70 27 0 36.8 0.340 27 0
765 5 121 72 23 112 26.2 0.245 30 0
766 1 126 60 0 0 30.1 0.349 47 1
767 1 93 70 31 0 30.4 0.315 23 0